Occlusion-free path planning with a probabilistic roadmap
Why this work is in the frame
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Bibliographic record
Abstract
We present a novel algorithm for path planning that avoids occlusions of a visual target for an ldquoeye-in-handrdquo sensor on an articulated robot arm. We compute paths using a probabilistic roadmap to avoid collisions between the robot and obstacles, while penalizing trajectories that do not maintain line-of-sight. The system determines the space from which line-of-sight is unimpeded to the target (the visible region). We assign penalties to trajectories within the roadmap proportional to the distance the camera travels while outside the visible region. Using Dijkstrapsilas algorithm, we compute paths of minimal occlusion (maximal visibility) through the roadmap. In our experiments, we compare a shortest-distance path to the minimal-occlusion path and discuss the impact of the improved visibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it